{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:VD5MDLSWXNBNRB3WSOVX3AW2DS","short_pith_number":"pith:VD5MDLSW","schema_version":"1.0","canonical_sha256":"a8fac1ae56bb42d8877693ab7d82da1caed376e205411512438f1ea846b3e97c","source":{"kind":"arxiv","id":"2105.11136","version":2},"attestation_state":"computed","paper":{"title":"Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiajie Zou, Jieyu Lin, Nai Ding","submitted_at":"2021-05-24T07:35:56Z","abstract_excerpt":"Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. Here, we demonstrate a simple yet effective method to attack MRC models and reveal the statistical biases in these models. We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options. It is found that several pre-trained language models, including BERT, ALBERT, and RoBERTa, show consistent prefer"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2105.11136","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-05-24T07:35:56Z","cross_cats_sorted":[],"title_canon_sha256":"5eff233f2263d8f523290dbb533064aaaf0608280b90de069f82b80a8b4670ec","abstract_canon_sha256":"0f3c8366c8c8197380fed95723f6c3a629101d2678836ea722b037c8b3fca692"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:42:52.507328Z","signature_b64":"YnnWFcVmX9eXAsgfRQD7VW6tPGW/3ojlY4U84TebWb8efe9KxOQPfPZIGTez/x8VZmqi7OUsXZNvNuew13JfDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8fac1ae56bb42d8877693ab7d82da1caed376e205411512438f1ea846b3e97c","last_reissued_at":"2026-07-05T02:42:52.506868Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:42:52.506868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiajie Zou, Jieyu Lin, Nai Ding","submitted_at":"2021-05-24T07:35:56Z","abstract_excerpt":"Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. Here, we demonstrate a simple yet effective method to attack MRC models and reveal the statistical biases in these models. We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options. It is found that several pre-trained language models, including BERT, ALBERT, and RoBERTa, show consistent prefer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.11136","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2105.11136/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2105.11136","created_at":"2026-07-05T02:42:52.506923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.11136v2","created_at":"2026-07-05T02:42:52.506923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.11136","created_at":"2026-07-05T02:42:52.506923+00:00"},{"alias_kind":"pith_short_12","alias_value":"VD5MDLSWXNBN","created_at":"2026-07-05T02:42:52.506923+00:00"},{"alias_kind":"pith_short_16","alias_value":"VD5MDLSWXNBNRB3W","created_at":"2026-07-05T02:42:52.506923+00:00"},{"alias_kind":"pith_short_8","alias_value":"VD5MDLSW","created_at":"2026-07-05T02:42:52.506923+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.13761","citing_title":"Innocence in the Crossfire: Roles of Skip Connections in Jailbreaking Visual Language Models","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS","json":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS.json","graph_json":"https://pith.science/api/pith-number/VD5MDLSWXNBNRB3WSOVX3AW2DS/graph.json","events_json":"https://pith.science/api/pith-number/VD5MDLSWXNBNRB3WSOVX3AW2DS/events.json","paper":"https://pith.science/paper/VD5MDLSW"},"agent_actions":{"view_html":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS","download_json":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS.json","view_paper":"https://pith.science/paper/VD5MDLSW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.11136&json=true","fetch_graph":"https://pith.science/api/pith-number/VD5MDLSWXNBNRB3WSOVX3AW2DS/graph.json","fetch_events":"https://pith.science/api/pith-number/VD5MDLSWXNBNRB3WSOVX3AW2DS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS/action/storage_attestation","attest_author":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS/action/author_attestation","sign_citation":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS/action/citation_signature","submit_replication":"https://pith.science/pith/VD5MDLSWXNBNRB3WSOVX3AW2DS/action/replication_record"}},"created_at":"2026-07-05T02:42:52.506923+00:00","updated_at":"2026-07-05T02:42:52.506923+00:00"}